Microwave power transfer (MPT) enables long-distance wireless energy delivery, but its practical deployment is constrained by electromagnetic field (EMF) overexposure to humans and electronic devices. This study aims to realize safe and real-time MPT by forming broad null regions that suppress EMF exposure while preserving power-transfer efficiency toward the receiver. To this end, we propose an efficient null-broadening beamforming method that directly updates excitation weights in the excitation-vector domain, thereby avoiding repeated full array-factor recomputation in conventional iterative approaches. Simulation results show that the proposed method achieves the target null depth with low computational latency and negligible mainlobe degradation. Furthermore, evaluations confirm that it effectively reduces EMF exposure in protected regions while maintaining receiver performance, demonstrating its practicality for real-time and safety-aware MPT systems.

Microwave power transfer (MPT) is attracting increasing interest as a promising solution for supplying power to wireless devices. This study aims to achieve the non-cooperative coexistence of co-located independent MPT systems in the same frequency channel with an eye to ward widespread deployment. The achievement of effective non-cooperative coexistence can provide flexibility when deploying MPT systems in environments where multiple independent MPT systems are operational, such as factories and apartment complexes. However, when energy transmitters and receivers from independent MPT systems are in close proximity, co-channel interference (CCI) occurs either between a pilot signal for channel estimation and power transmission signal, or between pilot signals, which in turn reduces the power transmission efficiency. This study investigates the application of the spread spectrum technique to pilot signals to suppress CCI. We characterize three spreading techniques: direct sequence, frequency hopping, and chirp spread spectrum schemes. Simulations and experiments show that the frequency hopping scheme achieves superior performance compared with other methods, particularly in terms of interference from power transmission. Measurements of the beamforming gain under the power transmission interference with a signal-to-interference ratio of -20 dB demonstrate that the frequency hopping method achieves a gain that is only 0.2 dB less than the theoretical value with a pilot signal length of 0.14 ms. In the presence of a carrier frequency offset of 1 ppm, the effect of the offset can be suppressed by increasing the signal length to 0.3 ms.

Microwave power transfer (MPT) must efficiently deliver power to mobile receivers while suppressing electromagnetic-field leakage into protected regions. This paper presents a subarrayed antenna architecture (SAA) designed for exposure-aware MPT. The proposed SAA separates intra-subarray beamforming, which provides localized broad-null controllability, from distributed subarray placement, which provides deployment diversity over the receiver service area. A genetic algorithm (GA) serves as an offline design tool to jointly optimize subarray positions and power allocation under a worst-case charging-to-protection metric evaluated over the receiver service area.We performed numerical simulations and anechoic-chamber measurements using a 16-element, four-subarray prototype at 5.75 GHz. The measurements were conducted at receiver points identified as worst-case locations by simulation. Numerical simulations show that the adopted GA-SAA improves Rmin from 2.87 dB for the baseline SAA deployment to 12.98 dB. Anechoic-chamber measurements show that the corresponding measured ratio Rmeas min at the simulation-derived worst-case receiver point improves from 2.64 dB to 10.60 dB, with an improvement of 7.96 dB.

Reconfigurable intelligent surfaces (RIS) have been attracting attention as a technology to expand coverage, particularly in millimeter-wave communication systems, which are highly susceptible to blockages. RIS can reflect radio waves in arbitrary directions by controlling the reflecting elements, which are numerous to enhance gain. However, while an increase in the number of elements leads to higher gain, it also results in a narrower beamwidth, thereby reducing the effectiveness of coverage expansion. In this study, we aim to extend coverage by expanding the beamwidth through the multiplication of chirp sequences with the reflection phase of each element. This method allows for rapid beamwidth expansion, as the computation is performed solely through matrix multiplication, offering a faster solution compared to conventional methods. The expansion of the beamwidth was confirmed through simulations and experiments using a 28 GHz, 400 -element RIS.

This study proposes a subarraying strategy for phased arrays in microwave power transfer, aimed at ensuring both wide-area high-efficiency power transfer and protection for targets such as humans and communication devices. We introduce a distributed array antenna deployment, distinct from conventional microwave power transfer antenna arrays, and explore the approach for position distribution and power allocation design. Simulation results show that the proposed deployment effectively enhances power transfer efficiency and target protection, outperforming traditional antenna deployments.

Distributed microwave power transfer (DMPT) systems maximize transfer efficiency by controlling the excitation phases of each transmitter to ensure that the received signals are in phase at the receiver. In this study, we investigated a deep-learning model that estimates the position of the receiver using the phase information of DMPT transmitters in an indoor environment. A convolutional neural network-based model with a residual network architecture achieved position estimation with an accuracy of less than 5 cm, even when considering the effects of multipaths. Furthermore, we demonstrated by fine-tuning, the model can adapt to changes in the indoor environment, resulting in improved performance.

In the next-generation smart factories, not only wireless data transmission but also wireless power transfer is required to enable the continuous operation of wireless sensors. To achieve both wireless information transfer (WIT) and wireless power transfer (WPT) within the constraints of limited frequency resources, it is essential to accurately recognize the locationdependent characteristics of the wireless environment. In this paper, we propose a method for estimating the radio environment corresponding to various antenna directivity patterns by constructing a radio environment database based on limited measured data and incorporating antenna directivity information. We measured the received power of a wireless power transmitter with a fixed antenna pattern in a smart factory and constructed a database. Then, the radio environment for different antenna directivity patterns is estimated using the measurement data and the corresponding antenna directivity information. Using the proposed method, the estimation error of the received power at reception points for wireless power transfer can be reduced.

This paper proposes modulation and demodulation techniques for establishing backscatter communication links in the satellite Internet of Things (IoT) using synthetic aperture radar (SAR) signals. The proposed method employs Manchester-coded pseudo-random sequences to mitigate interference with the primary SAR functionality while ensuring that the desired backscattered signals can be separated from surface-scattered waves. During backscattering, an IoT device modulates the pulses transmitted by the SAR satellite at each azimuth observation time with binary phase-shift keying (BPSK) according to Manchester-coded pseudo-random sequences. Each backscattered pulse corresponds to one bit of the sequence. Computational simulations using Gold sequences confirmed that the proposed method enables accurate decoding of the target sequence, limits the signal intensity in the single-look complex (SLC) image of the modulated wave to approximately 5%, and reduces the influence of surface-cattered waves during demodulation to approximately 4%.

Homomorphic encryption has garnered significant attention in addressing the critical need for privacy and security in facial recognition systems. Although this technique enhances data confidentiality, its practical application remains challenging owing to its substantial computational overhead and significant processing delays. This paper proposes an approach integrating locality-sensitive hashing (LSH) to accelerate the authentication process. LSH is a computational technique designed to identify similar data points in high-dimensional spaces. We utilize LSH to pre-classify similar facial features, effectively narrowing the search space and reducing the computational cost during authentication. This approach delivers significantly improved processing speeds and enhanced scalability to accommodate larger databases. In an experiment with 250 registered users, the conventional system had an average response time of 4.957±0.256 s, whereas the LSH-enhanced system reduced it to 0.307±0.135 s. This research contributes to improving the practicality of homomorphic encryption-based facial recognition systems.

This paper evaluates a computationally efficient beamforming method that employs broad nulls to enhance electromagnetic field (EMF) safety in microwave power transfer (MPT). Simulation and experimental results in an actual factory environment demonstrate that the method effectively suppresses EMF exposure in null regions while preserving high mainlobe directivity and maintaining low computational cost, validating the method’s suitability for practical real-time MPT.

Microwave power transfer (MPT) is a promising technology, but its high-power signals pose a significant interference challenge to co-located low-power communication systems such as Wi-Fi. To address this, we demonstrate an interference suppression scheme in which an MPT transmitter sniffs Wi-Fi frames and uses the extracted channel state information (CSI) to form a null in a Wi-Fi device. However, the accuracy of this scheme is limited by hardware-induced phase offsets. Therefore, to compensate, a two-step calibration method was employed. In a proof-of-concept experiment, this calibration reduced phase estimation error from over 20 degrees to under 5 degrees which improved the null depth by up to 11.0 dB for a total interference suppression of over 20 dB. This work experimentally verifies that null-steering based on sniffed frames is a viable strategy for MPT and Wi-Fi coexistence.

Site-specific path loss prediction is crucial for Beyond 5G/6G systems. Deep learning (DL) has attracted attention as a prediction method that utilizes detailed terrain information, but it can suffer from insufficient data volume and diversity. This paper evaluates two terrain data augmentation (DA) techniques—Tx-Rx Swap (TR-Swap), leveraging reciprocity, and Left-Right Swap (LR-Swap), based on spatial symmetry—to enhance DL-based path loss prediction. Using a CNN-BiLSTM model trained on 28 GHz ray-tracing data from a 3D Tokyo model, we assessed these methods via 4-fold cross-validation on three datasets of different scales. The evaluation revealed that LR-Swap significantly improved prediction accuracy, reducing root mean square error (RMSE) by approximately 1.0 dB with the smaller-scale dataset and by approximately 0.6 dB with the larger-scale dataset. TR-Swap, however, showed limited benefit, likely due to generated data characteristics mismatching the original training and test distributions. These findings demonstrate that LR-Swap effectively enhances DL-based path loss prediction by diversifying learned spatial features along the path, proving particularly impactful when dealing with scarce datasets. This highlights the significant potential of well-chosen DA strategies for improving model performance in this domain.

In this study, we propose a novel reconfigurable intelligent surface (RIS) beam tracking system using stereo camera images for multiple users. The aim is to enable fast and fair received signal strength (RSS) beam tracking for multiple users. In this system, beam tracking is enabled by continuously performing two workflows of user recognition by using stereo camera images and RIS phase control based on the users' positions. In this method, identical RSS beam tracking is enabled by calculating the weighted sums of the RIS phases for multiple users by utilizing distance and angle information of users from the RIS. We evaluated the proposed method experimentally in an anechoic chamber using RIS operating at 28 GHz and two human phantoms representing users. The results of the experiments validated that the proposed method achieve multi-user beam tracking with high RSS improvement and a small RSS deviation between multiple users.

In this chapter, the design methods of multi-hop wireless power transmission are described. First, the characteristics of multi-hop wireless power transmission are overviewed. Based on that, a design method based on bandpass filter (BPF) theory and a design method for realizing efficiency maximization in arbitrary hop are derived. Power efficiencies with the design theories presented here are evaluated in Section 4.6.

Conventional wireless power transfer technologies primarily focus on wireless charging devices, overlooking the impact of mechanical connections, such as cable tension on user interfaces. In contrast, we are exploring a wireless power transfer system with force-display functions to enable users to intuitively perceive the power supply status of electronic devices. The system used a permanent magnet on the receiver side and an electromagnet on the transmitter side. The charging status of the electronic device can be intuitively displayed to the user by adjusting the electromagnet based on the circumstances. In this paper, we present the design and implementation of single-core dual coils that realize both electromagnetic and wireless power transfer coils in a single core. Through experimental evaluations, the generation of attractive and repulsive forces up to approximately 2 N was confirmed in terms of the electromagnetic force. In addition, the maximum power transfer efficiency reached 87%.

In recent years, fault diagnosis in the 5G core network (5GC) has been widely investigated using machine learning (ML) due to the increasing requirement for adaptability and robustness in 5GC management algorithms. However, ML-based solutions often encounter challenges such as limited fault data availability and low adaptability to changing environments. Transfer learning (TL) offers a promising solution by transferring knowledge from a source domain to a target domain. This paper presents a hybrid transfer learning-based network fault diagnosis approach for the 5GC network, involving model-based TL, instance-based TL, and feature-based TL. Unlike the traditional single model-based TL, our method utilizes the inherent knowledge in 5GC network data and prevents negative transfer in features and data instances. We build a scale-out 5G testbed to generate sufficient source domain data while collecting a small amount of target domain data from a real 5GC network setting. We conduct network fault classification among different virtualized network functions (VNFs) in 5GC. We found that, compared to methods without TL and conventional model-based TL, hybrid TL provides a significantly higher accuracy in terms of F1 score. Furthermore, for each network label, 70 % of the labels achieve a F1 score higher than 0.9.

In principle, the distance of wireless power transfer (WPT) via radio waves from a transmitting antenna to a receiving one can be extended if the medium of wave propagation is lossless, e.g., vacuum in space. Technically, the distance limitation of WPT via radio waves is dominated by the power required by users as well as the limitation of system size and efficiency in practical applications. It is easy to develop multiuser WPT systems using WPT via radio waves because they are electromagnetically uncoupled and no interference between circuit parameters at a transmitter occurs even with increasing number of users. Besides, since the beam efficiency between a transmitting antenna and a receiving one is not high, multiuser WPT systems are also suitable when WPT systems via radio waves are applied in far field.

Homomorphic encryption is gaining attention for enhancing privacy and data security in facial recognition systems. However, utilizing homomorphic encryption poses a significant challenge in that the computational complexity reduces the processing speed. In this paper, we propose a novel approach to leverage the K-means algorithm to pre-cluster facial feature data. In this approach, clustering is performed on the cloud server, and authentication information in clusters is compared in the order of clusters that are most likely to contain the authentication target. This speeds up the recognition process and significantly increases the overall processing speed. By splitting a database of 500 registered individuals into 6 clusters, our system completed the recognition in an average of 2.64 sec., achieving a 365% improvement in speed. This result facilitates faster facial recognition while preserving data security, thereby enhancing the practicality of homomorphic encryption-based facial recognition systems.

We aim for the non-cooperative coexistence of colocated independent microwave power transfer (MPT) systems in the same frequency channel. When energy transmitters and receivers from independent MPT systems are in close proximity, co-channel interference occurs between a pilot signal for channel estimation and a power transmission signal or between pilot signals, which in turn reduces the power transmission efficiency. This study investigates spread spectrum pilot signals to suppress the co-channel interference between MPT systems. The simulation and experimental results demonstrate the effectiveness of the spread spectrum pilot signals and confirm the superiority of the frequency hopping spread spectrum technique.

Inductive power transfer (IPT) systems transmit power via magnetic field, therefore electromagnetic compatibility (EMC) and electromagnetic interference (EMI) issues are caused by the magnetic field leakage from the IPT systems. In this study, we propose a method to cancel both the fundamental and harmonic components of the magnetic field leakage in a multiple-input single-output (MISO) system by only controlling the transmitters and the receiver. The fundamental component of the input voltage and the optimum load impedance are derived to maximize power transmission efficiency (PTE) under the cancellation condition of the magnetic field leakage at the fundamental frequency. The harmonic components of the input voltage that can cancel the harmonics of the magnetic field leakage is calculated by estimating the harmonic components generated by the full-bridge rectifier on the receiver side. Circuit simulations were performed to evaluate the proposed method. As a result of the simulations, it was confirmed that the fundamental and harmonic components of the magnetic field leakage can be suppressed by more than 34 dB compared to the PTE maximization method, and that the reduction in PTE was about 1.7 %.